ranklab
Differentiable ranking, learning-to-rank losses, and IR evaluation metrics, backed by a Rust implementation for low overhead per call.
Python bindings for the rankit Rust crate.
Install
pip install ranklab
Quick start
import ranklab
# Differentiable soft ranking
scores = [5.0, 1.0, 2.0, 4.0, 3.0]
ranks = ranklab.soft_rank(scores, temperature=1.0)
# RankNet pairwise loss
predictions = [0.8, 0.3, 0.6]
relevance = [2.0, 0.0, 1.0]
loss = ranklab.ranknet_loss(predictions, relevance)
# NDCG evaluation
ranked = [("doc1", 0.9), ("doc2", 0.8), ("doc3", 0.7)]
qrels = {"doc1": 2, "doc2": 1, "doc3": 0}
score = ranklab.ndcg(ranked, qrels, k=3)
API
| Name | Description |
|---|---|
soft_rank |
Soft ranking via optimal-transport relaxation |
soft_rank_neural_sort |
Soft ranking via NeuralSort relaxation |
soft_rank_sigmoid |
Soft ranking via sigmoid pairwise comparisons |
soft_rank_smooth_i |
Soft ranking via smooth indicator functions |
differentiable_topk |
Differentiable top-k selection, returns (values, indicators) |
ranknet_loss |
RankNet pairwise cross-entropy loss |
approx_ndcg |
Differentiable NDCG approximation via softmax |
lambda_loss |
LambdaLoss ranking loss |
listnet_loss |
ListNet top-1 probability loss (KL divergence) |
listmle_loss |
ListMLE likelihood loss for permutation learning |
compute_lambdarank_gradients |
Per-document LambdaRank gradients |
compute_ranking_svm_gradients |
Per-document RankingSVM gradients |
ndcg |
Normalized Discounted Cumulative Gain at k |
map_score |
Mean Average Precision |
mrr |
Mean Reciprocal Rank |
precision_at_k |
Precision at depth k |
recall_at_k |
Recall at depth k |
err_at_k |
Expected Reciprocal Rank at depth k |
rbp_at_k |
Rank-Biased Precision at depth k |
f_measure_at_k |
F-measure (harmonic mean of P and R) at depth k |
success_at_k |
Whether any relevant doc appears in top-k |
r_precision |
Precision at R (R = number of relevant docs) |
average_precision |
Average Precision for binary relevance |
paired_t_test |
Paired t-test on two score sets, returns dict |
confidence_interval |
Confidence interval for a score set |
cohens_d |
Cohen's d effect size |
load_trec_run |
Parse a TREC run file into {query_id: [(doc_id, score)]} |
load_qrels |
Parse a TREC qrels file into {query_id: {doc_id: rel}} |
evaluate_batch |
Batch evaluation across multiple queries |
evaluate_trec |
Evaluate TREC run+qrels files with batch metrics |
numpy support
Scoring and loss functions accept numpy arrays or Python lists. Gradient functions return numpy float32 arrays. Eval metrics take list[tuple[str, float]] for ranked results and dict[str, int] for relevance judgments.
License
MIT OR Apache-2.0
1 maintainer
Release files for ranklab 0.1.1
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Built distributions (wheels)
Total release size: 13.4 MB
Release files / ranklab-0.1.1-pp311-pypy311_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
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